| //+------------------------------------------------------------------+
|
| //| Research.mq5 |
|
| //| Copyright DNG® |
|
| //| https://www.mql5.com/ru/users/dng |
|
| //+------------------------------------------------------------------+
|
| #property copyright "Copyright DNG®"
|
| #property link "https://www.mql5.com/ru/users/dng"
|
| #property version "1.00"
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| #include "Trajectory.mqh"
|
| //+------------------------------------------------------------------+
|
| //| Input parameters |
|
| //+------------------------------------------------------------------+
|
| input int Iterations = 1000;
|
| input int DiscriminatorBatch = 500;
|
| input int AgentBatch = 500;
|
| input int SchedulerBatch = 500;
|
| input float DiscountFactor = 0.99f;
|
| bool TrainMode = true;
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| STrajectory Buffer[];
|
| CFQF Actor;
|
| CFQF Scheduler;
|
| CNet Discriminator;
|
| int Models = 1;
|
| //---
|
| float dError;
|
| datetime dtStudied;
|
| //---
|
| CBufferFloat State;
|
| CBufferFloat StateSkill;
|
| CBufferFloat *Result;
|
| vector<float> ActorResult;
|
| vector<float> SchedulerResult;
|
| vector<float> DiscriminatorResult;
|
| //+------------------------------------------------------------------+
|
| //| Expert initialization function |
|
| //+------------------------------------------------------------------+
|
| int OnInit()
|
| {
|
| //---
|
| ResetLastError();
|
| if(!LoadTotalBase())
|
| {
|
| PrintFormat("Error of load study data: %d", GetLastError());
|
| return INIT_FAILED;
|
| }
|
| //--- load models
|
| float temp;
|
| if(!Actor.Load(FileName + "Act.nnw", dtStudied, true) ||
|
| !Scheduler.Load(FileName + "Sch.nnw", dtStudied, true) ||
|
| !Discriminator.Load(FileName + "Dscr.nnw", temp, temp, temp, dtStudied, true))
|
| {
|
| CArrayObj *actor = new CArrayObj();
|
| CArrayObj *scheduler = new CArrayObj();
|
| CArrayObj *discriminator = new CArrayObj();
|
| if(!CreateDescriptions(actor, scheduler, discriminator))
|
| {
|
| delete actor;
|
| delete scheduler;
|
| delete discriminator;
|
| return INIT_FAILED;
|
| }
|
| if(!Actor.Create(actor) ||
|
| !Scheduler.Create(scheduler) ||
|
| !Discriminator.Create(discriminator))
|
| {
|
| delete actor;
|
| delete scheduler;
|
| delete discriminator;
|
| return INIT_FAILED;
|
| }
|
| delete actor;
|
| delete scheduler;
|
| delete discriminator;
|
| //---
|
| #ifdef FileName
|
| if(!Actor.UpdateTarget(FileName + ".nnw") ||
|
| !Scheduler.UpdateTarget(FileName + ".nnw"))
|
| #else
|
| if(!Actor.UpdateTarget("FQF.upd") ||
|
| !Scheduler.UpdateTarget("FQF.nnw"))
|
| #endif
|
| return INIT_FAILED;
|
| }
|
| //---
|
| Discriminator.getResults(DiscriminatorResult);
|
| if(DiscriminatorResult.Size() != NSkills * AccountDescr)
|
| {
|
| PrintFormat("The scope of the discriminator does not match the skills count (%d <> %d)", NSkills, Result.Total());
|
| return INIT_FAILED;
|
| }
|
| Scheduler.getResults(SchedulerResult);
|
| Scheduler.SetUpdateTarget(MathMax(Iterations*SchedulerBatch / 100, 500000 / SchedulerBatch));
|
| if(SchedulerResult.Size() != NSkills)
|
| {
|
| PrintFormat("The scope of the scheduler does not match the skills count (%d <> %d)", NSkills, Result.Total());
|
| return INIT_FAILED;
|
| }
|
| Actor.getResults(ActorResult);
|
| Actor.SetUpdateTarget(MathMax(Iterations*AgentBatch / 100, 500000 / AgentBatch * NSkills));
|
| if(ActorResult.Size() != NActions)
|
| {
|
| PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
|
| return INIT_FAILED;
|
| }
|
| //---
|
| if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
|
| {
|
| PrintFormat("Error of create study event: %d", GetLastError());
|
| return INIT_FAILED;
|
| }
|
| //---
|
| return(INIT_SUCCEEDED);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Expert deinitialization function |
|
| //+------------------------------------------------------------------+
|
| void OnDeinit(const int reason)
|
| {
|
| //---
|
| Actor.Save(FileName + "Act.nnw", TimeCurrent(), true);
|
| Scheduler.Save(FileName + "Sch.nnw", TimeCurrent(), true);
|
| Discriminator.Save(FileName + "Dscr.nnw", 0, 0, 0, TimeCurrent(), true);
|
| delete Result;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| ChartEvent function |
|
| //+------------------------------------------------------------------+
|
| void OnChartEvent(const int id,
|
| const long &lparam,
|
| const double &dparam,
|
| const string &sparam)
|
| {
|
| //---
|
| if(id == 1001)
|
| Train();
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Saves TotalBase. |
|
| //+------------------------------------------------------------------+
|
| bool SaveTotalBase(void)
|
| {
|
| int total = ArraySize(Buffer);
|
| if(total < 0)
|
| return true;
|
| int handle = FileOpen(FileName + ".bd", FILE_WRITE | FILE_BIN | FILE_COMMON);
|
| if(handle < 0)
|
| return false;
|
| if(FileWriteInteger(handle, total) < INT_VALUE)
|
| {
|
| FileClose(handle);
|
| return false;
|
| }
|
| for(int i = 0; i < total; i++)
|
| if(!Buffer[i].Save(handle))
|
| {
|
| FileClose(handle);
|
| return false;
|
| }
|
| FileFlush(handle);
|
| FileClose(handle);
|
| //---
|
| return true;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Loads TotalBase. |
|
| //+------------------------------------------------------------------+
|
| bool LoadTotalBase(void)
|
| {
|
| int handle = FileOpen(FileName + ".bd", FILE_READ | FILE_BIN | FILE_COMMON | FILE_SHARE_READ);
|
| if(handle < 0)
|
| return false;
|
| int total = FileReadInteger(handle);
|
| if(total <= 0)
|
| {
|
| FileClose(handle);
|
| return false;
|
| }
|
| if(ArrayResize(Buffer, total) < total)
|
| {
|
| FileClose(handle);
|
| return false;
|
| }
|
| for(int i = 0; i < total; i++)
|
| if(!Buffer[i].Load(handle))
|
| {
|
| FileClose(handle);
|
| return false;
|
| }
|
| FileClose(handle);
|
| //---
|
| return true;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Train function |
|
| //+------------------------------------------------------------------+
|
| void Train(void)
|
| {
|
| int total_tr = ArraySize(Buffer);
|
| uint ticks = GetTickCount();
|
| vector<float> account, reward;
|
| int bar, action;
|
| int skill, shift;
|
| //---
|
| for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
|
| {
|
| for(int phase = 0; phase < 3; phase++)
|
| {
|
| int batch = 0;
|
| switch(phase)
|
| {
|
| case 0:
|
| batch = DiscriminatorBatch;
|
| break;
|
| case 1:
|
| batch = AgentBatch;
|
| break;
|
| case 2:
|
| batch = SchedulerBatch;
|
| break;
|
| default:
|
| PrintFormat("Incorrect phase %d");
|
| batch = 0;
|
| break;
|
| }
|
| for(int batch_iter = 0; batch_iter < batch; batch_iter++)
|
| {
|
| int tr = (int)(((double)MathRand() / 32767.0) * (total_tr - 1));
|
| int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
|
| State.AssignArray(Buffer[tr].States[i].state);
|
| float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
|
| float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
|
| State.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
|
| State.Add(Buffer[tr].States[i].account[1] / PrevBalance);
|
| State.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
|
| State.Add(Buffer[tr].States[i].account[2] / PrevBalance);
|
| State.Add(Buffer[tr].States[i].account[4] / PrevBalance);
|
| State.Add(Buffer[tr].States[i].account[5]);
|
| State.Add(Buffer[tr].States[i].account[6]);
|
| State.Add(Buffer[tr].States[i].account[7] / PrevBalance);
|
| State.Add(Buffer[tr].States[i].account[8] / PrevBalance);
|
| //---
|
| bar = (HistoryBars - 1) * BarDescr;
|
| double cl_op = Buffer[tr].States[i + 1].state[bar];
|
| // Implements SymbolInfoDouble.
|
| double prof_1l = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_PROFIT) * cl_op /
|
| SymbolInfoDouble(_Symbol, SYMBOL_POINT);
|
| PrevBalance = Buffer[tr].States[i].account[0];
|
| PrevEquity = Buffer[tr].States[i].account[1];
|
| if(IsStopped())
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| //---
|
| switch(phase)
|
| {
|
| case 0:
|
| if(!Discriminator.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| for(skill = 0; skill < NSkills; skill++)
|
| {
|
| SchedulerResult = vector<float>::Zeros(NSkills);
|
| SchedulerResult[skill] = 1;
|
| StateSkill.AssignArray(GetPointer(State));
|
| StateSkill.AddArray(SchedulerResult);
|
| if(IsStopped())
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| if(!Actor.feedForward(GetPointer(State), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| break;
|
| }
|
| action = Actor.getSample();
|
| account = GetNewState(Buffer[tr].States[i].account, action, prof_1l);
|
| shift = skill * AccountDescr;
|
| DiscriminatorResult[shift] = (account[0] - PrevBalance) / PrevBalance;
|
| DiscriminatorResult[shift + 1] = account[1] / PrevBalance;
|
| DiscriminatorResult[shift + 2] = (account[1] - PrevEquity) / PrevEquity;
|
| DiscriminatorResult[shift + 3] = account[2] / PrevBalance;
|
| DiscriminatorResult[shift + 4] = account[4] / PrevBalance;
|
| DiscriminatorResult[shift + 5] = account[5];
|
| DiscriminatorResult[shift + 6] = account[6];
|
| DiscriminatorResult[shift + 7] = account[7] / PrevBalance;
|
| DiscriminatorResult[shift + 8] = account[8] / PrevBalance;
|
| }
|
| if(!Result)
|
| {
|
| Result = new CBufferFloat();
|
| if(!Result)
|
| {
|
| PrintFormat("Error of create buffer %d", GetLastError());
|
| ExpertRemove();
|
| break;
|
| }
|
| }
|
| Result.AssignArray(DiscriminatorResult);
|
| if(!Discriminator.backProp(Result,(CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| break;
|
| case 1:
|
| if(!Discriminator.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| Discriminator.getResults(DiscriminatorResult);
|
| for(skill = 0; skill < NSkills; skill++)
|
| {
|
| SchedulerResult = vector<float>::Zeros(NSkills);
|
| SchedulerResult[skill] = 1;
|
| StateSkill.AssignArray(GetPointer(State));
|
| StateSkill.AddArray(SchedulerResult);
|
| if(IsStopped())
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| if(!Actor.feedForward(GetPointer(State), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| reward = GetAgentReward(skill, DiscriminatorResult, Buffer[tr].States[i].account);
|
| Result.AssignArray(reward);
|
| StateSkill.AssignArray(Buffer[tr].States[i + 1].state);
|
| account = GetNewState(Buffer[tr].States[i].account, Actor.getAction(), prof_1l);
|
| shift = skill * AccountDescr;
|
| StateSkill.Add((account[0] - PrevBalance) / PrevBalance);
|
| StateSkill.Add(account[1] / PrevBalance);
|
| StateSkill.Add((account[1] - PrevEquity) / PrevEquity);
|
| StateSkill.Add(account[2] / PrevBalance);
|
| StateSkill.Add(account[4] / PrevBalance);
|
| StateSkill.Add(account[5]);
|
| StateSkill.Add(account[6]);
|
| StateSkill.Add(account[7] / PrevBalance);
|
| StateSkill.Add(account[8] / PrevBalance);
|
| if(!Actor.backProp(Result, DiscountFactor, GetPointer(StateSkill), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| }
|
| break;
|
| case 2:
|
| if(!Scheduler.feedForward(GetPointer(State), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| Scheduler.getResults(SchedulerResult);
|
| State.AddArray(SchedulerResult);
|
| if(IsStopped())
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| if(!Actor.feedForward(GetPointer(State), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| action = Actor.getAction();
|
| account = GetNewState(Buffer[tr].States[i].account, action, prof_1l);
|
| SchedulerResult = SchedulerResult * (account[0] / PrevBalance - 1.0);
|
| Result.AssignArray(SchedulerResult);
|
| State.AssignArray(Buffer[tr].States[i + 1].state);
|
| State.Add((account[0] - PrevBalance) / PrevBalance);
|
| State.Add(account[1] / PrevBalance);
|
| State.Add((account[1] - PrevEquity) / PrevEquity);
|
| State.Add(account[2] / PrevBalance);
|
| State.Add(account[4] / PrevBalance);
|
| State.Add(account[5]);
|
| State.Add(account[6]);
|
| State.Add(account[7] / PrevBalance);
|
| State.Add(account[8] / PrevBalance);
|
| if(!Scheduler.backProp(Result, DiscountFactor, GetPointer(State), 1, false))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| break;
|
| }
|
| break;
|
| default:
|
| PrintFormat("Wrong phase %d", phase);
|
| break;
|
| }
|
| if(GetTickCount() - ticks > 500)
|
| {
|
| string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Scheduler", iter * 100.0 / (double)(Iterations), Scheduler.getRecentAverageError());
|
| str += StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Discriminator", iter * 100.0 / (double)(Iterations), Discriminator.getRecentAverageError());
|
| Comment(str);
|
| ticks = GetTickCount();
|
| }
|
| }
|
| }
|
| }
|
| Comment("");
|
| //---
|
| PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Scheduler", Scheduler.getRecentAverageError());
|
| PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Discriminator", Discriminator.getRecentAverageError());
|
| ExpertRemove();
|
| //---
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Gets NewState. |
|
| //+------------------------------------------------------------------+
|
| vector<float> GetNewState(float &prev_account[], int action, double prof_1l)
|
| {
|
| vector<float> result;
|
| //---
|
| result.Assign(prev_account);
|
| switch(action)
|
| {
|
| case 0:
|
| result[5] += (float)SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
|
| result[7] += result[5] * (float)prof_1l;
|
| result[8] -= result[6] * (float)prof_1l;
|
| result[4] = result[7] + result[8];
|
| result[1] = result[0] + result[4];
|
| break;
|
| case 1:
|
| result[6] += (float)SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
|
| result[7] += result[5] * (float)prof_1l;
|
| result[8] -= result[6] * (float)prof_1l;
|
| result[4] = result[7] + result[8];
|
| result[1] = result[0] + result[4];
|
| break;
|
| case 2:
|
| result[0] += result[4];
|
| result[1] = result[0];
|
| result[2] = result[0];
|
| for(int i = 3; i < AccountDescr; i++)
|
| result[i] = 0;
|
| break;
|
| case 3:
|
| result[7] += result[5] * (float)prof_1l;
|
| result[8] -= result[6] * (float)prof_1l;
|
| result[4] = result[7] + result[8];
|
| result[1] = result[0] + result[4];
|
| break;
|
| }
|
| //--- return result
|
| return result;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Gets AgentReward. |
|
| //+------------------------------------------------------------------+
|
| vector<float> GetAgentReward(int skill, vector<float> &discriminator, float &prev_account[])
|
| {
|
| //--- prepare
|
| matrix<float> discriminator_matrix;
|
| discriminator_matrix.Init(1, discriminator.Size());
|
| discriminator_matrix.Row(discriminator, 0);
|
| discriminator_matrix.Reshape(NSkills, AccountDescr);
|
| vector<float> forecast = discriminator_matrix.Row(skill);
|
| //--- check action
|
| int action = 3;
|
| float buy = forecast[5] - prev_account[5];
|
| float sell = forecast[6] - prev_account[6];
|
| if(buy < 0 && sell < 0)
|
| action = 2;
|
| else
|
| if(buy > sell)
|
| action = 0;
|
| else
|
| if(buy < sell)
|
| action = 1;
|
| //--- calculate reward
|
| vector<float> result = vector<float>::Zeros(NActions);
|
| float mean = (forecast / discriminator_matrix.Mean(0)).Mean();
|
| result[action] = MathLog(MathAbs(mean));
|
| //--- return result
|
| return result;
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
